Mark C. Vanderwel

Plant functional traits have globally consistent effects on competition

Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits—wood density, specific leaf area and maximum height—consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges

1. Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. 2. Forests are changing over a wide range of spatio-temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land-use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio-temporal scales are needed to further our understanding. 3. Combinations of experiments, observations and process-based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. 4. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management.

Quantifying variation in forest disturbance, and its effects on aboveground biomass dynamics, across the eastern United States

The role of tree mortality in the global carbon balance is complicated by strong spatial and temporal heterogeneity that arises from the stochastic nature of carbon loss through disturbance. Characterizing spatio-temporal variation in mortality (including disturbance) and its effects on forest and carbon dynamics is thus essential to understanding the current global forest carbon sink, and to predicting how it will change in future. We analyzed forest inventory data from the eastern United States to estimate plot-level variation in mortality (relative to a long-term background rate for individual trees) for nine distinct forest regions. Disturbances that produced at least a fourfold increase in tree mortality over an approximately 5 year interval were observed in 1–5% of plots in each forest region. The frequency of disturbance was lowest in the northeast, and increased southwards along the Atlantic and Gulf coasts as fire and hurricane disturbances became progressively more common. Across the central and northern parts of the region, natural disturbances appeared to reflect a diffuse combination of wind, insects, disease, and ice storms. By linking estimated covariation in tree growth and mortality over time with a data-constrained forest dynamics model, we simulated the implications of stochastic variation in mortality for long-term aboveground biomass changes across the eastern United States. A geographic gradient in disturbance frequency induced notable differences in biomass dynamics between the least- and most-disturbed regions, with variation in mortality causing the latter to undergo considerably stronger fluctuations in aboveground stand biomass over time. Moreover, regional simulations showed that a given long-term increase in mean mortality rates would support greater aboveground biomass when expressed through disturbance effects compared with background mortality, particularly for early-successional species. The effects of increased tree mortality on carbon stocks and forest composition may thus depend partly on whether future mortality increases are chronic or episodic in nature.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Abstract Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the worlds forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

How Stand Productivity Results from Size- and Competition-Dependent Growth and Mortality

Background A better understanding of the relationship between stand structure and productivity is required for the development of: a) scalable models that can accurately predict growth and yield dynamics for the worlds forests; and b) stand management regimes that maximize wood and/or timber yield, while maintaining structural and species diversity. Methods We develop a cohort-based canopy competition model (“CAIN”), parameterized with inventory data from Ontario, Canada, to examine the relationship between stand structure and productivity. Tree growth, mortality and recruitment are quantified as functions of diameter and asymmetric competition, using a competition index (CAIh) defined as the total projected area of tree crowns at a given trees mid-crown height. Stand growth, mortality, and yield are simulated for inventoried stands, and also for hypothetical stands differing in total volume and tree size distribution. Results For a given diameter, tree growth decreases as CAIh increases, whereas the probability of mortality increases. For a given CAIh, diameter growth exhibits a humped pattern with respect to diameter, whereas mortality exhibits a U-shaped pattern reflecting senescence of large trees. For a fixed size distribution, stand growth increases asymptotically with total density, whereas mortality increases monotonically. Thus, net productivity peaks at an intermediate volume of 100–150 m3/ha, and approaches zero at 250 m3/ha. However, for a fixed stand volume, mortality due to senescence decreases if the proportion of large trees decreases as overall density increases. This size-related reduction in mortality offsets the density-related increase in mortality, resulting in a 40% increase in yield. Conclusions Size-related variation in growth and mortality exerts a profound influence on the relationship between stand structure and productivity. Dense stands dominated by small trees yield more wood than stands dominated by fewer large trees, because the relative growth rate of small trees is higher, and because they are less likely to die.

Global convergence in leaf respiration from estimates of thermal acclimation across time and space

Recent compilations of experimental and observational data have documented global temperature-dependent patterns of variation in leaf dark respiration (R), but it remains unclear whether local adjustments in respiration over time (through thermal acclimation) are consistent with the patterns in R found across geographical temperature gradients. We integrated results from two global empirical syntheses into a simple temperature-dependent respiration framework to compare the measured effects of respiration acclimation-over-time and variation-across-space to one another, and to a null model in which acclimation is ignored. Using these models, we projected the influence of thermal acclimation on: seasonal variation in R; spatial variation in mean annual R across a global temperature gradient; and future increases in R under climate change. The measured strength of acclimation-over-time produces differences in annual R across spatial temperature gradients that agree well with global variation-across-space. Our models further project that acclimation effects could potentially halve increases in R (compared with the null model) as the climate warms over the 21st Century. Convergence in global temperature-dependent patterns of R indicates that physiological adjustments arising from thermal acclimation are capable of explaining observed variation in leaf respiration at ambient growth temperatures across the globe.

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delive...

Landscape‐scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

Demographic controls of aboveground forest biomass across North America

Ecologists have limited understanding of how geographic variation in forest biomass arises from differences in growth and mortality at continental to global scales. Using forest inventories from across North America, we partitioned continental-scale variation in biomass growth and mortality rates of 49 tree species groups into (1) species-independent spatial effects and (2) inherent differences in demographic performance among species. Spatial factors that were separable from species composition explained 83% and 51% of the respective variation in growth and mortality. Moderate additional variation in mortality (26%) was attributable to differences in species composition. Age-dependent biomass models showed that variation in forest biomass can be explained primarily by spatial gradients in growth that were unrelated to species composition. Species-dependent patterns of mortality explained additional variation in biomass, with forests supporting less biomass when dominated by species that are highly susceptible to competition (e.g. Populus spp.) or to biotic disturbances (e.g. Abies balsamea).

Using a data-constrained model of home range establishment to predict abundance in spatially heterogeneous habitats.

Mechanistic modelling approaches that explicitly translate from individual-scale resource selection to the distribution and abundance of a larger population may be better suited to predicting responses to spatially heterogeneous habitat alteration than commonly-used regression models. We developed an individual-based model of home range establishment that, given a mapped distribution of local habitat values, estimates species abundance by simulating the number and position of viable home ranges that can be maintained across a spatially heterogeneous area. We estimated parameters for this model from data on red-backed vole (Myodes gapperi) abundances in 31 boreal forest sites in Ontario, Canada. The home range model had considerably more support from these data than both non-spatial regression models based on the same original habitat variables and a mean-abundance null model. It had nearly equivalent support to a non-spatial regression model that, like the home range model, scaled an aggregate measure of habitat value from local associations with habitat resources. The home range and habitat-value regression models gave similar predictions for vole abundance under simulations of light- and moderate-intensity partial forest harvesting, but the home range model predicted lower abundances than the regression model under high-intensity disturbance. Empirical regression-based approaches for predicting species abundance may overlook processes that affect habitat use by individuals, and often extrapolate poorly to novel habitat conditions. Mechanistic home range models that can be parameterized against abundance data from different habitats permit appropriate scaling from individual- to population-level habitat relationships, and can potentially provide better insights into responses to disturbance.